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Tablero Coronavirus: Caso Venezuela
Este tablero [Tablero Coronavirus: Caso Venezuela] proporciona una visión de los efectos de 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemia para Venezuela. Este tablero fue construido usando la interfaz de R Markdown y es una adaptación de la información proviniente del tablero dashboard by Rami Krispin.
Código
El código utilizado para la reproducción de este tablero se obtuvo del código publicado por Antoine Soetewey, para mas información dirigirse a la dirección de GitHub: GitHub.
Datos
Los datos utilizados para la reproducción de este tablero provienen
del conjunto de datos disponibles en el paquete de R {coronavirus}. Asegurar de descargar la
versión en desarrollo para obtener la ultima version disponibles de los
datos:
install.packages("devtools")
devtools::install_github("RamiKrispin/coronavirus")
Los datos primarios o sin procesar provienen de:
Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus repository.
Información
Para mayor información acerca de como reproducir este tablero dirigirse al artículo publicado por el autor del código ; article.
Actualización
La ultima versión disponible de los datos tienen fecha Tuesday May 24, 2022 y este tablero fue actualizado Friday May 27, 2022.
Dirigirse statsandr.com (blog del autor del código) ó antoinesoetewey.com (website del autor del código).
---
title: "Incidencia coronavirus en Venezuela"
author: "José Alirio Cardoza"
output:
flexdashboard::flex_dashboard:
orientation: rows
# social: ["facebook", "twitter", "linkedin"]
source_code: embed
vertical_layout: fill
---
```{r setup, include=FALSE}
#------------------ Packages ------------------
library(flexdashboard)
# install.packages("devtools")
# devtools::install_github("RamiKrispin/coronavirus", force = TRUE)
library(coronavirus)
data(coronavirus)
# View(coronavirus)
# max(coronavirus$date)
`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "purple"
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "red"
#------------------ Data ------------------
df <- coronavirus %>%
# dplyr::filter(date == max(date)) %>%
dplyr::filter(country == "Venezuela") %>%
dplyr::group_by(country, type) %>%
dplyr::summarise(total = sum(cases)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
# dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
dplyr::arrange(-confirmed) %>%
dplyr::ungroup() %>%
dplyr::mutate(country = dplyr::if_else(country == "United Arab Emirates", "UAE", country)) %>%
dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
dplyr::mutate(country = trimws(country)) %>%
dplyr::mutate(country = factor(country, levels = country))
df_daily <- coronavirus %>%
dplyr::filter(country == "Venezuela") %>%
dplyr::group_by(date, type) %>%
dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
dplyr::arrange(date) %>%
dplyr::ungroup() %>%
#dplyr::mutate(active = confirmed - death - recovered) %>%
dplyr::mutate(active = confirmed - death) %>%
dplyr::mutate(
confirmed_cum = cumsum(confirmed),
death_cum = cumsum(death),
# recovered_cum = cumsum(recovered),
active_cum = cumsum(active)
)
df1 <- coronavirus %>% dplyr::filter(date == max(date))
```
# Resumen
## Row {data-width="400"}
### confirmed {.value-box}
```{r}
valueBox(
value = paste(format(sum(df$confirmed), big.mark = ","), "", sep = " "),
caption = "Contagios totales confirmados",
icon = "fas fa-user-md",
color = confirmed_color
)
```
### death {.value-box}
```{r}
valueBox(
value = paste(format(sum(df$death, na.rm = TRUE), big.mark = ","), " (",
round(100 * sum(df$death, na.rm = TRUE) / sum(df$confirmed), 1),
"%)",
sep = ""
),
caption = "Número total de fallecidos (Tasa de fallecidos)",
icon = "fas fa-heart-broken",
color = death_color
)
```
## Row
### **Total de casos acumulados diariamente** (Venezuela)
```{r}
plotly::plot_ly(data = df_daily) %>%
plotly::add_trace(
x = ~date,
# y = ~active_cum,
y = ~confirmed_cum,
type = "scatter",
mode = "lines+markers",
# name = "Active",
name = "Contagios",
line = list(color = active_color),
marker = list(color = active_color)
) %>%
plotly::add_trace(
x = ~date,
y = ~death_cum,
type = "scatter",
mode = "lines+markers",
name = "Fallecimientos",
line = list(color = death_color),
marker = list(color = death_color)
) %>%
# plotly::add_annotations(
# x = as.Date("2020-02-04"),
# y = 1,
# text = paste("First case"),
# xref = "x",
# yref = "y",
# arrowhead = 5,
# arrowhead = 3,
# arrowsize = 1,
# showarrow = TRUE,
# ax = -10,
# ay = -90
# ) %>%
plotly::add_annotations(
x = as.Date("2020-03-26"),
y = 3,
text = paste("Primer deceso"),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = 60,
ay = -90
) %>%
plotly::add_annotations(
x = as.Date("2020-03-16"),
y = 14,
text = paste(
"Inicio Cuarentena"
),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = -80,
ay = -90
) %>%
plotly::layout(
title = "",
yaxis = list(title = "Números de casos acumulados"),
xaxis = list(title = "Fecha"),
legend = list(x = 0.1, y = 0.9),
hovermode = "compare"
)
```
# Tablas comparativas
## Column {data-width="400"}
### **Cantidad de contagios diarios confirmados **
```{r}
daily_confirmed <- coronavirus %>%
dplyr::filter(type == "confirmed") %>%
dplyr::filter(date >= "2020-02-29") %>%
dplyr::mutate(country = country) %>%
dplyr::group_by(date, country) %>%
dplyr::summarise(total = sum(cases)) %>%
dplyr::ungroup() %>%
tidyr::pivot_wider(names_from = country, values_from = total)
#----------------------------------------
# Plotting the data
daily_confirmed %>%
plotly::plot_ly() %>%
plotly::add_trace(
x = ~date,
y = ~Venezuela,
type = "scatter",
mode = "lines+markers",
name = "Venezuela"
) %>%
# plotly::add_trace(
# x = ~date,
# y = ~France,
# type = "scatter",
# mode = "lines+markers",
# name = "France"
# ) %>%
# plotly::add_trace(
# x = ~date,
# y = ~Spain,
# type = "scatter",
# mode = "lines+markers",
# name = "Spain"
# ) %>%
plotly::add_trace(
x = ~date,
y = ~Colombia,
type = "scatter",
mode = "lines+markers",
name = "Colombia"
) %>%
plotly::add_trace(
x = ~date,
y = ~Brazil,
type = "scatter",
mode = "lines+markers",
name = "Peru"
) %>%
plotly::add_trace(
x = ~date,
y = ~Argentina,
type = "scatter",
mode = "lines+markers",
name = "Argentina"
) %>%
plotly::layout(
title = "",
legend = list(x = 0.7, y = 0.9),
yaxis = list(title = "Número de contagios por fecha"),
xaxis = list(title = "Fecha"),
# paper_bgcolor = "black",
# plot_bgcolor = "black",
# font = list(color = 'white'),
hovermode = "compare",
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
)
)
```
### **Distribución de casos por tipo**
```{r daily_summary}
df_EU <- coronavirus %>%
# dplyr::filter(date == max(date)) %>%
dplyr::filter(country == "Venezuela" |
country == "Colombia" |
country == "Peru" |
country == "Argentina") %>%
dplyr::group_by(country, type) %>%
dplyr::summarise(total = sum(cases)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
# dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
dplyr::arrange(confirmed) %>%
dplyr::ungroup() %>%
dplyr::mutate(country = dplyr::if_else(country == "United Arab Emirates", "UAE", country)) %>%
dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
dplyr::mutate(country = trimws(country)) %>%
dplyr::mutate(country = factor(country, levels = country))
plotly::plot_ly(
data = df_EU,
x = ~country,
# y = ~unrecovered,
y = ~ confirmed,
# text = ~ confirmed,
# textposition = 'auto',
type = "bar",
name = "Contagios",
marker = list(color = active_color)
) %>%
plotly::add_trace(
y = ~death,
# text = ~ death,
# textposition = 'auto',
name = "Fallecimientos",
marker = list(color = death_color)
) %>%
plotly::layout(
barmode = "stack",
yaxis = list(title = "Total de contagios y decesos"),
xaxis = list(title = ""),
hovermode = "compare",
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
)
)
```
# Mapa mundial
### **Comportamiento del COVID-19 resto del mundo** (*use + and - icons to zoom in/out*)
```{r}
# map tab added by Art Steinmetz
library(leaflet)
library(leafpop)
library(purrr)
cv_data_for_plot <- coronavirus %>%
# dplyr::filter(country == "Venezuela") %>%
dplyr::filter(cases > 0) %>%
dplyr::group_by(country, province, lat, long, type) %>%
dplyr::summarise(cases = sum(cases)) %>%
dplyr::mutate(log_cases = 2 * log(cases)) %>%
dplyr::ungroup()
cv_data_for_plot.split <- cv_data_for_plot %>% split(cv_data_for_plot$type)
pal <- colorFactor(c("orange", "red", "green"), domain = c("confirmed", "death", "recovery"))
map_object <- leaflet() %>% addProviderTiles(providers$Stamen.Toner)
names(cv_data_for_plot.split) %>%
purrr::walk(function(df) {
map_object <<- map_object %>%
addCircleMarkers(
data = cv_data_for_plot.split[[df]],
lng = ~long, lat = ~lat,
# label=~as.character(cases),
color = ~ pal(type),
stroke = FALSE,
fillOpacity = 0.8,
radius = ~log_cases,
popup = leafpop::popupTable(cv_data_for_plot.split[[df]],
feature.id = FALSE,
row.numbers = FALSE,
zcol = c("type", "cases", "country", "province")
),
group = df,
# clusterOptions = markerClusterOptions(removeOutsideVisibleBounds = F),
labelOptions = labelOptions(
noHide = F,
direction = "auto"
)
)
})
map_object %>%
addLayersControl(
overlayGroups = names(cv_data_for_plot.split),
options = layersControlOptions(collapsed = FALSE)
)
```
# Información
**Tablero Coronavirus: Caso Venezuela**
Este tablero [Tablero Coronavirus: Caso Venezuela] proporciona una visión de los efectos de 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemia para Venezuela. Este tablero fue construido usando la interfaz de R Markdown y es una adaptación de la información proviniente del tablero [dashboard](https://ramikrispin.github.io/coronavirus_dashboard/){target="_blank"} by Rami Krispin.
**Código**
El código utilizado para la reproducción de este tablero se obtuvo del código publicado por Antoine Soetewey, para mas información dirigirse a la dirección de GitHub: [GitHub](https://github.com/AntoineSoetewey/coronavirus_dashboard){target="_blank"}.
**Datos**
Los datos utilizados para la reproducción de este tablero provienen del conjunto de datos disponibles en el paquete de R [`{coronavirus}`](https://github.com/RamiKrispin/coronavirus){target="_blank"}. Asegurar de descargar la versión en desarrollo para obtener la ultima version disponibles de los datos:
install.packages("devtools")
devtools::install_github("RamiKrispin/coronavirus")
Los datos primarios o sin procesar provienen de:
Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus [repository](https://github.com/RamiKrispin/coronavirus-csv){target="_blank"}.
**Información**
Para mayor información acerca de como reproducir este tablero dirigirse al artículo publicado por el autor del código ; [article](https://statsandr.com/blog/how-to-create-a-simple-coronavirus-dashboard-specific-to-your-country-in-r/).
**Actualización**
La ultima versión disponible de los datos tienen fecha `r format(max(coronavirus$date), "%A %B %d, %Y")` y este tablero fue actualizado `r format(Sys.time(), "%A %B %d, %Y")`.
*Dirigirse [statsandr.com](https://statsandr.com/) (blog del autor del código) ó [antoinesoetewey.com](https://www.antoinesoetewey.com/) (website del autor del código)*.